Industrial equipment requires constant maintenance to remain in peak operating condition. Traditionally, as equipment failed it was repaired or replaced based on the most economically appropriate course of action. Preventative maintenance was limited to lubricating moving components and replacing components based on a timed schedule. The practice of timed component replacement was subject to replacing components not yet in need of replacement or waiting too long and incurring the catastrophic failure of the component, possibly incurring greater production downtime, increased risk of personnel injury, increased consequential damage and loss, along with added replacement costs.
The development of sensor technology such as vibration measurement sensors allowed for the implementation of systems to more accurately predict a component failure based on an increase in vibration of the equipment over time or a characteristic change in the vibration frequency spectrum. Although this technology reduced the cost of maintenance and downtime, it introduced other problems associated with installing instrumentation on equipment in remote locations. It proved expensive and sometimes difficult to provide power to the instrumentation. Additionally, wiring for data communication with the instruments was costly and in many instances impractical because of the physical location, machinery movement, or hazardous environment associated with the operation of the equipment.
A solution to the problems associated with the data wiring was found with the advent of wireless networks. This technology allowed transmission of the collected data from the equipment to a location in the local area more conducive to power and data wiring. This solution however did not solve the problem of providing power to operate the sensor, processor and transmitter for the sensing device. Partially addressing this problem was the evolution of battery technology with greater storage capacity. This development allowed sensors to operate on battery power and eliminate the requirement of running power cables to the sensor location.
As before, the new solution once again introduces new problems of operation. The batteries require exchange on a frequency based on the power consumption and at the end of their useful life required appropriate disposal. Replacing batteries in hard to reach or dangerous locations, near operating equipment, or in hazardous environments often presents unacceptable risks for individuals and for companies. Furthermore, the discharge characteristics of batteries make it difficult to determine the amount of charge remaining in the battery. Batteries with unused power are typically discarded and replaced with new batteries. The cost, logistics, and disposal of batteries have an important economic and environmental impact. All of these activities equate to costs in many cases making the implementation prohibitively expensive.
The above scenario for powering remote sensors for machinery condition monitoring can be extended to the case for operating remote sensors, processors, actuators, data storage, wireless communications, and logic to provide not only enhanced machinery monitoring and protection, but also for surveillance, mobile systems monitoring, and remote control. This model is applicable to a single remote sensor node and also to multiple sensor-processor nodes operating to monitor components or systems and to exchange information and collaborate among the remote sensor-processor nodes. The processing logic on a sensor-processor node could be of a category referred to as agents or intelligent agents. An agent is a software model that is an abstraction of real-world entities and often is operated with some local autonomy and communication capability to achieve local goals or objectives in concert with support overarching system objectives. A sensor-processor node may provide a platform for deploying multi-agent systems (MAS) where each sensor-processor node could correspond to one or more logical agents.
The recognition of the value of remote sensing technology and distributed processing to provide predictive information for equipment maintenance along with the high cost and inconvenience of either running power lines or providing a battery update regimen has created market pressure to design a system providing the benefits of self-powered wireless sensors without the added cost associated with powering the sensing device. Similarly, the need to deploy remote monitoring and control modules and the desire for distributed intelligent agents each without access to local, wired line power provides even greater need for remote, efficiently self-powered sensor-processor devices.